Data sparsity is a well-known limitation in the sonar domain. This limitation is a problem when applying data-intensive techniques from the computer vision community, such as deep learning models for detection and classification. One way of extending a sonar dataset is to use simulated sonar images however, these often have the drawback of looking non-realistic when compared to domain data. To overcome the data-sparsity problem as well as for generating realistic-looking sonar images, we introduce a pipeline where the possibilities and limitations of applying cycleGAN to enhance simulated forward-looking sonar images are explored. The results show improved classification performance when training a classifier on enhanced-simulated images compared to training on solely simulated images.